Additionally to the MNIST dataset we created the MNIST-R dataset containing all original MNIST images rotated by 30 degrees. We train a simple neural net with two 128- and one 20-width ReLU representations. MNIST is used as source while the performance is measured on MNIST-R (target). Inspired by Heinze-Deml et al. [1]} few samples of the target set are used to improve the performance. We compare the results in Tab. REF  with a baseline model trained on the same amount of samples (20 per class) of the target dataset using data augmentation. Our approach can keep up with data augmentation, respectively even performs better if the imbalance in the amount of samples used during training becomes larger. It can better leverage the structure in the source data and map it to the target domain than simple data augmentation which relies on predefined transformations.
[1][tb]<FIGURE>Accuracy of MNIST with different number of training samples from the target domain (MNIST-R). Our approach (orange) clearly outperforms the simple baseline, especially when only few data from the target domain is provided.
